IEEE Transactions on Control Systems Technology, 28(3):730-740, May 2020 (article)

Abstract

Bayesian optimization is proposed for automatic
learning of optimal controller parameters from experimental
data. A probabilistic description (a Gaussian process) is used
to model the unknown function from controller parameters to
a user-defined cost. The probabilistic model is updated with
data, which is obtained by testing a set of parameters on the
physical system and evaluating the cost. In order to learn fast,
the Bayesian optimization algorithm selects the next parameters
to evaluate in a systematic way, for example, by maximizing
information gain about the optimum. The algorithm thus iteratively
finds the globally optimal parameters with only few
experiments. Taking throttle valve control as a representative
industrial control example, the proposed auto-tuning method is
shown to outperform manual calibration: it consistently achieves
better performance with a low number of experiments. The
proposed auto-tuning framework is flexible and can handle
different control structures and objectives.

Bipedal animals have diverse morphologies and advanced locomotion abilities. Terrestrial birds, in particular, display agile, efficient, and robust running motion, in which they exploit the interplay between the body segment masses and moment of inertias. On the other hand, most legged robots are not able to generate such versatile and energy-efficient motion and often disregard trunk movements as a means to enhance their locomotion capabilities. Recent research investigated how trunk motions affect the gait characteristics of humans, but there is a lack of analysis across different bipedal morphologies. To address this issue, we analyze avian running based on a spring-loaded inverted pendulum model with a pronograde (horizontal) trunk. We use a virtual point based control scheme and modify the alignment of the ground reaction forces to assess how our control strategy influences the trunk pitch oscillations and energetics of the locomotion. We derive three potential key strategies to leverage trunk pitch motions that minimize either the energy fluctuations of the center of mass or the work performed by the hip and leg. We suggest how these strategies could be used in legged robotics.

Muscle models and animal observations suggest that physical damping is beneficial for stabilization. Still, only a few implementations of mechanical damping exist in compliant robotic legged locomotion. It remains unclear how physical damping can be exploited for locomotion tasks, while its advantages as sensor-free, adaptive force- and negative work-producing actuators are promising. In a simplified numerical leg model, we studied the energy dissipation from viscous and Coulomb damping during vertical drops with ground-level perturbations. A parallel spring-damper is engaged between touch-down and mid-stance, and its damper auto-disengages during mid-stance and takeoff. Our simulations indicate that an adjustable and viscous damper is desired. In hardware we explored effective viscous damping and adjustability and quantified the dissipated energy. We tested two mechanical, leg-mounted damping mechanisms; a commercial hydraulic damper, and a custom-made pneumatic damper. The pneumatic damper exploits a rolling diaphragm with an adjustable orifice, minimizing Coulomb damping effects while permitting adjustable resistance. Experimental results show that the leg-mounted, hydraulic damper exhibits the most effective viscous damping. Adjusting the orifice setting did not result in substantial changes of dissipated energy per drop, unlike adjusting damping parameters in the numerical model. Consequently, we also emphasize the importance of characterizing physical dampers during real legged impacts to evaluate their effectiveness for compliant legged locomotion.

Postural stability is one of the most crucial elements in bipedal
locomotion. Bipeds are dynamically unstable and need to maintain their
trunk upright against the rotations induced by the ground reaction forces
(GRFs), especially when running. Gait studies report that the GRF vectors
focus around a virtual point above the center of mass (VPA), while the trunk
moves forward in pitch axis during the stance phase of human running.
However, a recent simulation study suggests that a virtual point below the
center of mass (VPB) might be present in human running, since a VPA
yields backward trunk rotation during the stance phase. In this work, we
perform a gait analysis to investigate the existence and location of the
VP in human running at 5 m s−1, and support our findings numerically
using the spring-loaded inverted pendulum model with a trunk (TSLIP).
We extend our analysis to include perturbations in terrain height (visible
and camouflaged), and investigate the response of the VP mechanism
to step-down perturbations both experimentally and numerically. Our
experimental results show that the human running gait displays a VPB
of ≈ −30 cm and a forward trunk motion during the stance phase. The
camouflaged step-down perturbations affect the location of the VPB. Our
simulation results suggest that the VPB is able to encounter the step-down
perturbations and bring the system back to its initial equilibrium state.

Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

2016

The International Journal of Robotics Research, 35(14):1731-1749, December 2016 (article)

Abstract

The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. The GF represents the belief of the current state by a Gaussian distribution, whose mean is an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF as the solution to a constrained optimization problem. From this new perspective, the GF is seen as a special case of a much broader class of filters, obtained by relaxing the constraint on the form of the approximate posterior. On this basis, we outline some conditions which potential generalizations have to satisfy in order to maintain the computational efficiency of the GF. We propose one concrete generalization which corresponds to the standard GF using a pseudo measurement instead of the actual measurement. Extending an existing GF implementation in this manner is trivial. Nevertheless, we show that this small change can have a major impact on the estimation accuracy.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems